1b: This project will use the power, detailed phenotypic information, and prospective nature of UK Biobank to improve our understanding of the determinants of “less frequent” cardiovascular disease. This research is in the public interest considering population aging and the burden these diseases have on the adult population. Evidence concerning the determinants of these diseases could importantly inform public health priorities and the targeting of prevention efforts.

1c: We will analyse the genetics and non-genetic determinants of a range of “less-frequent” CVDs such as (but not limited to) venous thromboembolism (including deep vein thrombosis and pulmonary embolism), aortic aneurysms, peripheral arterial disease, atrial fibrillation and other arrhythmias, syncope, sudden cardiac death, and heart failure, and compare them with the determinants of CHD and stroke (including stroke subtypes). Furthermore, we will evaluate whether risk prediction models can be developed for CVDs either considering risk scores to predict each type of event separately or by using a single risk score model to predict the combined outcome of all such events.

1d: We will perform analyses in the full cohort.

Project extension:

Previous studies on cardiovascular disease (CVD) have tended to focus on a limited number of specific diseases, such as coronary heart disease (CHD) or stroke.

However, CHD and stroke currently represent only ~40% of incident cardiovascular events in UK, and other CVDs outcomes have received far less attention so far.

The objectives of this project are therefore:

1)To identify potential genetic and non-genetic (biochemical, lifestyle and other characteristics) determinants of a range of “less-frequent” CVDs.

We will apply cutting edge machine learning algorithms to explore distinct electrocardiogram signatures underlying less well known cardiovascular diseases to enable a more granular classification of specific sub-phenotypes of disease. Similarly, we will utilise radiomics, harnessing data from MRI scans in UK Biobank to help bridge the gap between raw imaging and personalized medicine to determine how, for example, such data can be used to improve the diagnostic, prognostic, and predictive accuracy of clinical-decision support systems for cardiovascular outcomes. All derived variables will be returned to UK Biobank.